How Many Words Do You Need to Know to Watch Friends?

A few years ago, The New York Times published an article about several Major League Baseball players who use the sitcom Friends to improve their English. Friends seems to be a very popular tool for learning English: there’s even an ESL program developed around it, and you can easily find advice on how to use Friends as a language learning tool.

In my own language learning I’ve relied heavily on frequency dictionaries, which order words by their popularity. Learning just the top 500-1,000 words in language goes surprisingly far in conversation.

I wanted to know how many English words someone would need to recognize to be able to watch Friends and understand, say, 90% of all the words.

Here the definition of “word” becomes important. For the purposes of this article, I’m going to consider most grammatical inflections of a root to be the same word. For example, “go”, “going”, and “went” will be considered one word, since they all relate back to the root word “to go”. Later on we’ll see how to operationalize this via lemmatization algorithms, which will allow us to map words back to their grammatical roots.

Fortunately, complete transcripts of Friends are available online. Below I go through the data collection and analysis. But first, here’s the final result:

You need to know 750 words to know 90% of all the words used in Friends

Around 750 words gets you to 90% coverage. Watching Friends where you don’t know every tenth word could be a frustrating experience, but hopefully context clues would help fill in gaps. The Appendix at the bottom of the post lists the top 750 words.

The numbers in this post should be considered as rough estimates because I did not filter out other words that we may not want to consider as words, like proper nouns (“Joey”, “Monica”, “New York”, etc.)

Data Collection

The script of every episode of Friends is available at https://fangj.github.io/friends/. I used rvest to download every file, and parsed the scripts into individual lines. I only wanted to have to do this once, so I saved the results to a CSV file so that subsequent analyses wouldn’t have to redownload all of the data.

library(tidyverse)
library(rvest)

pages <- read_html("https://fangj.github.io/friends/") %>%
  html_nodes("a") %>%
  html_attr("href")

pages <- str_c("https://fangj.github.io/friends/", pages)

parse_episode <- function(url) {
  read_html(url) %>%
    html_nodes("p:not([align=center])") %>%
    .[-1] %>%
    html_text() %>%
    enframe(name = NULL, value = "line") %>%
    mutate(
      url = url,
      episode = str_extract(url, "\\d{4}"), # extract episode number
      character = str_extract(line, "^(\\w+):"), # extract character name
      character = str_replace_all(character, ":$", ""),
      line = str_replace_all(line, "^\\w+: ", ""),
    ) %>%
    filter(line != "")
}

friends_raw <- pages %>% map(parse_episode) %>%
  bind_rows()

write_csv(friends_raw, "./friends.csv")

This yields a raw dataset with the lines from every episode:

head(friends_raw, n = 10) %>%
  select(episode, character, line) %>%
  gt::gt()
episode character line
0101 NA [Scene: Central Perk, Chandler, Joey, Phoebe, and Monica are there.]
0101 Monica There's nothing to tell! He's just some guy I work with!
0101 Joey C'mon, you're going out with the guy! There's gotta be something wrong with him!
0101 Chandler All right Joey, be nice.  So does he have a hump? A hump and a hairpiece?
0101 Phoebe Wait, does he eat chalk?
0101 NA (They all stare, bemused.)
0101 Phoebe Just, 'cause, I don't want her to go through what I went through with Carl- oh!
0101 Monica Okay, everybody relax. This is not even a date. It's just two people going out to dinner and- not having sex.
0101 Chandler Sounds like a date to me.
0101 NA [Time Lapse]

Cleaning

First, there are some lines that give stage directions which we want to filter out of the dataset, since we are only interested in spoken words. There are also directions given sometimes within a character’s line, which we want to filter.

friends <- friends_raw %>%
  mutate(
    line = str_replace_all(line, "[\u0091\u0092]", "'"),
    line = str_replace_all(line, "\u0097", "-"),
    line = str_replace_all(line, "\\([\\w\n [[:punct:]]]+\\)", ""),
    line = str_replace_all(line, "\\[[\\w\n [[:punct:]]]+\\]", ""),
    line = str_trim(line)
  ) %>%
  filter(line != "")

head(friends, n = 10) %>%
  select(episode, character, line) %>%
  gt::gt()
episode character line
0101 Monica There's nothing to tell! He's just some guy I work with!
0101 Joey C'mon, you're going out with the guy! There's gotta be something wrong with him!
0101 Chandler All right Joey, be nice.  So does he have a hump? A hump and a hairpiece?
0101 Phoebe Wait, does he eat chalk?
0101 Phoebe Just, 'cause, I don't want her to go through what I went through with Carl- oh!
0101 Monica Okay, everybody relax. This is not even a date. It's just two people going out to dinner and- not having sex.
0101 Chandler Sounds like a date to me.
0101 Chandler Alright, so I'm back in high school, I'm standing in the middle of the cafeteria, and I realize I am totally naked.
0101 All Oh, yeah. Had that dream.
0101 Chandler Then I look down, and I realize there's a phone... there.

Now we transform our data from a set of lines from every script into a format that is more useful for analysis. Using tidytext, we generate a new table that has every word from the corpus and how many times it was used. We also calculate cumulative sums and proportions.

# Counts how many times each word
# appears, sorts by number of appearances,
# and adds cumulative proportions
add_cumulative_stats <- function(x) {
  x %>%
    count(word, sort = TRUE) %>%
    mutate(cumsum = cumsum(n),
           cumprop = cumsum / sum(n),
           index = 1:n())
}

library(tidytext)

words <- friends %>%
  unnest_tokens(word, line)

word_counts <- add_cumulative_stats(words)

word_counts %>%
  head(n = 10) %>%
  gt::gt()
word n cumsum cumprop index
i 23298 23298 0.04076248 1
you 22017 45315 0.07928371 2
the 13292 58607 0.10253956 3
to 11554 70161 0.12275459 4
a 10749 80910 0.14156118 5
and 9405 90315 0.15801629 6
it 7631 97946 0.17136758 7
that 7564 105510 0.18460166 8
oh 7226 112736 0.19724436 9
what 6355 119091 0.20836315 10

From the cumulative proportions in the table we can see that knowing the top 10 words covers around 20% of all the words in the dataset.

This is a good start, but it’s not quite what we want. Right now the data includes grammatical variations of the same word as different words. For example, “age” and “ages” really represent the same word, but the graph above includes them as two separate words. As such, the data we have so far overestimates the number of unique words that appear in Friends.

Lemmatizing algorithms try to map every grammatical variation of a word to a “stem”. For example, the words “go”, “going”, and “went” should map to the stem “go”. Stemming algorithms are related, but work by removing suffixes from the ends of words. A stemming algorithm wouldn’t know to map “went” to “go”; for that, we need lemmatization, which uses dictionaries to reverse the inflected forms of words to its grammatical root. The package textstem provides a lemmatize_words function in R.

library(textstem)

word_lemmas <- words %>%
  mutate(word = lemmatize_words(word))

word_lemma_counts <- add_cumulative_stats(word_lemmas)

There are 11,852 words after lemmatization, compared to 15,134 before.

word_lemma_counts %>%
  head(n = 10) %>%
  gt::gt()
word n cumsum cumprop index
i 23298 23298 0.04076248 1
you 22364 45662 0.07989082 2
be 19053 64715 0.11322620 3
the 13292 78007 0.13648205 4
a 11566 89573 0.15671808 5
to 11554 101127 0.17693310 6
and 9405 110532 0.19338821 7
that 7993 118525 0.20737287 8
it 7631 126156 0.22072416 9
oh 7226 133382 0.23336687 10

The top 10 words are the same, but the cumulative proportions have changed since there are fewer words overall. Now knowing the top 10 words covers about 23% of all words.

A helper function helps compute how many words we need to know to cover a certain percentage of the total:

# How many words do you need to know prop% of all words?
words_for_prop <- function(x, prop) {
  x %>% filter(cumprop > prop) %>% pull(index) %>% first()
}

words_for_prop_result <- tibble(
  prop = c(0.98, 0.95, 0.9, 0.5),
  words = map_int(prop, function(x) words_for_prop(word_lemma_counts, x))
)

words_for_prop_result %>%
  gt() %>%
  fmt_percent(columns = vars(prop), decimals = 0) %>%
  fmt_number(columns = vars(words), use_seps = TRUE, decimals = 0) %>%
  cols_label(prop = "Cumulative percentage", words = "Number of words")
Cumulative percentage Number of words
98% 4,148
95% 1,758
90% 743
50% 46

Using the helper function, we can make a graph of the cumulative distribution of words with cutoffs at 50%, 90%, and 95%:

word_lemma_counts %>%
  ggplot(aes(x = index, y = cumprop * 100)) +
  geom_line(color = "#2980b9", size = 1) +
  geom_segment(aes(x = words, xend = words, y = 0, yend = prop * 100), lty = 2, data = words_for_prop_result, color = "#2c3e50") +
  geom_segment(aes(x = 0, xend = words, y = prop * 100, yend = prop * 100), lty = 2, data = words_for_prop_result, color = "#2c3e50") +
  geom_point(aes(x = words, y = prop * 100), data = words_for_prop_result, color = "white", size = 3) +
  geom_point(aes(x = words, y = prop * 100), data = words_for_prop_result, color = "#2c3e50", size = 2) +
  labs(x = "Words ordered by frequency", y = "Cumulative percentage") +
  cowplot::theme_cowplot() +
  scale_y_continuous(labels = function(x) paste0(x, "%"), breaks = c(0, 25, 50, 75, words_for_prop_result$prop * 100), expand = c(0.01, 0.01)) +
  scale_x_continuous(breaks = c(words_for_prop_result$words, 7500, 10000), expand = c(0.01, 0.01)) +
  my_theme +
  ggtitle("You need to know around 750 words to know 90%\nof all the words used in Friends") +
  labs(caption = "Analysis: Herb Susmann, transcripts: https://fangj.github.io/friends/")
You need to know 750 words to know 90% of all the words used in Friends

Figure 1: You need to know 750 words to know 90% of all the words used in Friends

Appendix: top 750 words

word_lemma_counts %>%
  mutate(`cumulative proportion` = scales::percent_format(0.01)(cumprop)) %>%
  select(index, n, word, `cumulative proportion`) %>%
  head(n = 750) %>%
  reactable()
Avatar
Herb Susmann
Graduate Student

Gradate student in Biostatistics at UMass Amherst matter.